Formal Framework and Models for Validation and Verification of Software-Intensive Aerospace Systems

Guarro, Sergio; Yau, Michael; Özgüner, Ü.; Aldemir, Tunc; Kurt, Arda; Hejase, Mohammad; Knudson, Matt

doi:10.2514/6.2017-0418

Cited by 17 publications

(14 citation statements)

References 9 publications

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“…The DFM allows for modelling of physical, functional, and dynamic characteristics of a system, with the aim to validate and analyse the design with respect to reliability and safety. The method can be used to assess the effect of a failure on the system behaviour and for backwards reasoning, i.e., inferring how a certain system behaviour may be produced 67 . The model is represented as a diagraph.…”

Section: 𝑃(𝐴|𝐵 = 𝑃(𝐵|𝐴)⋅𝑃(𝐴) 𝑃(𝐵)mentioning

confidence: 99%

“…The system's behaviour is modelled through discrete-time transitions among the cells. Transitions are modelled through a set of equations or algorithms that represent the physical and control laws the system is subjected to 56,57 . The technique has been developed for verification and validation of model-based control systems 56 .…”

Section: 𝑃(𝐴|𝐵 = 𝑃(𝐵|𝐴)⋅𝑃(𝐴) 𝑃(𝐵)mentioning

confidence: 99%

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Risk-informed control systems for improved operational performance and decision-making

Thieme

Rokseth

Utne

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Autonomous systems, including airborne, land-based, marine, and underwater vehicles, are increasingly present in the world. One important aspect of autonomy is the capability to process information and to make independent decisions for achieving a mission goal. Information on the level of risk related to the operation may improve the decision-making process of autonomous systems. This article describes the integration of risk analysis methods with the control system of autonomous and highly automated systems that are evaluated during operation. Four main areas of implementation are identified; (i) risk models used to directly make decisions, (ii) use of the output of risk models as input to decision-making and optimization algorithms, (iii) the output of risk models may be used as a constraint in or modifying constraints of algorithms, and (iv) the output of risk models may be used to inform representations or maps of the environment to be used in path planning. A case study on a dynamic positioning controller of an offshore supply vessel exemplifies the concepts described in this article. In addition, it demonstrates how risk model output may be used within a hybrid controller.

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Section: 𝑃(𝐴|𝐵 = 𝑃(𝐵|𝐴)⋅𝑃(𝐴) 𝑃(𝐵)mentioning

confidence: 99%

Section: 𝑃(𝐴|𝐵 = 𝑃(𝐵|𝐴)⋅𝑃(𝐴) 𝑃(𝐵)mentioning

confidence: 99%

Risk-informed control systems for improved operational performance and decision-making

Thieme

Rokseth

Utne

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…However, this expanded capability and flexibility, and the dynamic nature of the model-based design environment, also pose challenges to the execution of traditional design validation and verification (V&V) processes. It is, thus, essential that methods and tools be made available also to facilitate the task of demonstrating compliance of control systems developed in such model-based environments with safety-related requirements and any applicable certification standard [72].…”

Section: Safety and Security Of Cyber-physical Systemsmentioning

confidence: 99%

The future of risk assessment

Zio

2018

Reliability Engineering & System Safety

279

116

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A B S T R A C TRisk assessment must evolve for addressing the existing and future challenges, and considering the new systems and innovations that have already arrived in our lives and that are coming ahead. In this paper, I swing on the rapid changes and innovations that the World that we live in is experiencing, and analyze them with respect to the challenges that these pose to the field of risk assessment. Digitalization brings opportunities but with it comes also the complexity of cyber-phyiscal systems. Climate change and extreme natural events are increasingly threatening our infrastructures; terrorist and malevolent threats are posing severe concerns for the security of our systems and lives. These sources of hazard are extremely uncertain and, thus, difficult to describe and model quantitatively.Some research and development directions that are emerging are presented and discussed, also considering the ever increasing computational capabilities and data availability. These include the use of simulation for accident scenario identification and exploration, the extension of risk assessment into the framework of resilience and business continuity, the reliance on data for dynamic and condition monitoring-based risk assessment, the safety and security assessment of cyber-physical systems.The paper is not a research work and not exactly a review or a state of the art work, but rather it offers a lookout on risk assessment, open to consideration and discussion, as it cannot pretend to give an absolute point of view nor to be complete in the issues addressed (and the related literature referenced to).

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“…Based on the model based validation and verification framework described in [14,15] , a similar framework was constructed for the validation and verification of an autonomous ground vehicle controller. The framework, depicted in Fig.…”

Section: Overview Of Control System Design Frameworkmentioning

confidence: 99%

“…Generally speaking, existing approaches either have challenges with accurately capturing high-fidelity system dynamics, or when incorporating possible random component failures and configuration changes. The algorithm has already been used in a validation and verification framework development for Unmanned Aircraft Systems (UAS) as part of the System-Wide Safety Assurance Technologies (SSAT) initiative taken by the National Aeronautics and Space Administration (NASA) which the authors conducted jointly with ASCA, Inc. [14,15,16,17]. Sections 2 and 3 of the paper provide, respectively, overviews of the autonomous ground vehicle controller design framework the analysis in this paper is based on.…”

Section: Introductionmentioning

confidence: 99%

Identification of Risk Significant Automotive Scenarios Under Hardware Failures

Hejase

Kurt

Aldemir

et al. 2018

Electron. Proc. Theor. Comput. Sci.

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The level of autonomous functions in vehicular control systems has been on a steady rise. This rise makes it more challenging for control system engineers to ensure a high level of safety, especially against unexpected failures such as stochastic hardware failures. A generic Backtracking Process Algorithm (BPA) based on a deductive implementation of the Markov/Cell-to-Cell Mapping technique is proposed for the identification of critical scenarios leading to the violation of safety goals. A discretized state-space representation of the system allows tracing of fault propagation throughout the system, and the quantification of probabilistic system evolution in time. A case study of a Hybrid State Control System for an autonomous vehicle prone to a brake-by-wire failure is constructed. The hazard of interest is collision with a stationary vehicle. The BPA is implemented to identify the risk significant scenarios leading to the hazard of interest.

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Formal Framework and Models for Validation and Verification of Software-Intensive Aerospace Systems

Cited by 17 publications

References 9 publications

Risk-informed control systems for improved operational performance and decision-making

Risk-informed control systems for improved operational performance and decision-making

The future of risk assessment

Identification of Risk Significant Automotive Scenarios Under Hardware Failures

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